Overview

Dataset statistics

Number of variables22
Number of observations27042
Missing cells0
Missing cells (%)0.0%
Duplicate rows54
Duplicate rows (%)0.2%
Total size in memory5.8 MiB
Average record size in memory223.1 B

Variable types

Numeric21
Categorical1

Alerts

Dataset has 54 (0.2%) duplicate rowsDuplicates
longitude is highly overall correlated with latitude and 4 other fieldsHigh correlation
Temp_pre_30 is highly overall correlated with Temp_pre_15 and 4 other fieldsHigh correlation
Temp_pre_15 is highly overall correlated with Temp_pre_30 and 6 other fieldsHigh correlation
Temp_pre_7 is highly overall correlated with Temp_pre_30 and 10 other fieldsHigh correlation
Temp_cont is highly overall correlated with Temp_pre_30 and 9 other fieldsHigh correlation
Wind_pre_30 is highly overall correlated with Temp_pre_7 and 7 other fieldsHigh correlation
Wind_pre_15 is highly overall correlated with Temp_pre_7 and 6 other fieldsHigh correlation
Wind_pre_7 is highly overall correlated with Temp_pre_15 and 9 other fieldsHigh correlation
Wind_cont is highly overall correlated with Temp_pre_7 and 8 other fieldsHigh correlation
Hum_pre_30 is highly overall correlated with longitude and 12 other fieldsHigh correlation
Hum_pre_15 is highly overall correlated with longitude and 12 other fieldsHigh correlation
Hum_pre_7 is highly overall correlated with longitude and 11 other fieldsHigh correlation
Hum_cont is highly overall correlated with Temp_pre_7 and 6 other fieldsHigh correlation
Prec_pre_30 is highly overall correlated with Prec_pre_15High correlation
Prec_pre_15 is highly overall correlated with Prec_pre_30 and 2 other fieldsHigh correlation
Prec_pre_7 is highly overall correlated with Prec_pre_15High correlation
Prec_cont is highly overall correlated with Prec_pre_15High correlation
remoteness is highly overall correlated with latitude and 4 other fieldsHigh correlation
latitude is highly overall correlated with longitude and 2 other fieldsHigh correlation
Vegetation is highly overall correlated with latitudeHigh correlation
Prec_pre_30 is highly skewed (γ1 = 52.34920555)Skewed
Vegetation has 4156 (15.4%) zerosZeros
Temp_pre_30 has 1150 (4.3%) zerosZeros
Temp_pre_15 has 1881 (7.0%) zerosZeros
Temp_pre_7 has 2209 (8.2%) zerosZeros
Temp_cont has 9186 (34.0%) zerosZeros
Wind_pre_30 has 1318 (4.9%) zerosZeros
Wind_pre_15 has 2056 (7.6%) zerosZeros
Wind_pre_7 has 2383 (8.8%) zerosZeros
Wind_cont has 9255 (34.2%) zerosZeros
Hum_pre_30 has 1721 (6.4%) zerosZeros
Hum_pre_15 has 2448 (9.1%) zerosZeros
Hum_pre_7 has 2764 (10.2%) zerosZeros
Hum_cont has 9435 (34.9%) zerosZeros
Prec_pre_30 has 9628 (35.6%) zerosZeros
Prec_pre_15 has 11040 (40.8%) zerosZeros
Prec_pre_7 has 13242 (49.0%) zerosZeros
Prec_cont has 14564 (53.9%) zerosZeros

Reproduction

Analysis started2022-12-01 09:13:28.125595
Analysis finished2022-12-01 09:15:40.797391
Duration2 minutes and 12.67 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

fire_size
Real number (ℝ)

Distinct301
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5715125
Minimum0.51
Maximum3.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size422.5 KiB
2022-12-01T02:15:41.053743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.51
5-th percentile0.75
Q11
median1.1
Q32
95-th percentile3
Maximum3.49
Range2.98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7466901
Coefficient of variation (CV)0.47514106
Kurtosis-0.65702354
Mean1.5715125
Median Absolute Deviation (MAD)0.38
Skewness0.77976165
Sum42496.841
Variance0.55754611
MonotonicityNot monotonic
2022-12-01T02:15:41.386839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10864
40.2%
2 5376
19.9%
3 3210
 
11.9%
1.5 1275
 
4.7%
2.5 512
 
1.9%
0.8 409
 
1.5%
0.7 334
 
1.2%
0.6 315
 
1.2%
0.75 307
 
1.1%
1.2 205
 
0.8%
Other values (291) 4235
 
15.7%
ValueCountFrequency (%)
0.51 27
 
0.1%
0.52 21
 
0.1%
0.53 22
 
0.1%
0.54 33
 
0.1%
0.55 32
 
0.1%
0.56 17
 
0.1%
0.57 16
 
0.1%
0.58 20
 
0.1%
0.59 21
 
0.1%
0.6 315
1.2%
ValueCountFrequency (%)
3.49 4
< 0.1%
3.48 3
< 0.1%
3.4716 1
 
< 0.1%
3.47 4
< 0.1%
3.46 3
< 0.1%
3.45 4
< 0.1%
3.44 2
< 0.1%
3.43 4
< 0.1%
3.42 4
< 0.1%
3.41 4
< 0.1%

stat_cause_descr
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size422.5 KiB
Debris Burning
8267 
Miscellaneous
4610 
Arson
4342 
Missing/Undefined
2439 
Lightning
2094 
Other values (8)
5290 

Length

Max length17
Median length14
Mean length11.461985
Min length5

Characters and Unicode

Total characters309955
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArson
2nd rowDebris Burning
3rd rowMiscellaneous
4th rowDebris Burning
5th rowCampfire

Common Values

ValueCountFrequency (%)
Debris Burning 8267
30.6%
Miscellaneous 4610
17.0%
Arson 4342
16.1%
Missing/Undefined 2439
 
9.0%
Lightning 2094
 
7.7%
Equipment Use 2057
 
7.6%
Children 908
 
3.4%
Smoking 731
 
2.7%
Campfire 684
 
2.5%
Railroad 516
 
1.9%
Other values (3) 394
 
1.5%

Length

2022-12-01T02:15:41.681028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debris 8267
22.1%
burning 8267
22.1%
miscellaneous 4610
12.3%
arson 4342
11.6%
missing/undefined 2439
 
6.5%
lightning 2094
 
5.6%
equipment 2057
 
5.5%
use 2057
 
5.5%
children 908
 
2.4%
smoking 731
 
2.0%
Other values (5) 1594
 
4.3%

Most occurring characters

ValueCountFrequency (%)
n 40904
13.2%
i 37887
12.2%
s 28889
 
9.3%
e 28682
 
9.3%
r 23555
 
7.6%
g 15625
 
5.0%
u 15038
 
4.9%
l 10861
 
3.5%
o 10541
 
3.4%
10324
 
3.3%
Other values (25) 87649
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 257387
83.0%
Uppercase Letter 39805
 
12.8%
Space Separator 10324
 
3.3%
Other Punctuation 2439
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 40904
15.9%
i 37887
14.7%
s 28889
11.2%
e 28682
11.1%
r 23555
9.2%
g 15625
 
6.1%
u 15038
 
5.8%
l 10861
 
4.2%
o 10541
 
4.1%
b 8267
 
3.2%
Other values (11) 37138
14.4%
Uppercase Letter
ValueCountFrequency (%)
D 8267
20.8%
B 8267
20.8%
M 7049
17.7%
U 4496
11.3%
A 4342
10.9%
L 2094
 
5.3%
E 2057
 
5.2%
C 1592
 
4.0%
S 783
 
2.0%
R 516
 
1.3%
Other values (2) 342
 
0.9%
Space Separator
ValueCountFrequency (%)
10324
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2439
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 297192
95.9%
Common 12763
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 40904
13.8%
i 37887
12.7%
s 28889
 
9.7%
e 28682
 
9.7%
r 23555
 
7.9%
g 15625
 
5.3%
u 15038
 
5.1%
l 10861
 
3.7%
o 10541
 
3.5%
D 8267
 
2.8%
Other values (23) 76943
25.9%
Common
ValueCountFrequency (%)
10324
80.9%
/ 2439
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 309955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 40904
13.2%
i 37887
12.2%
s 28889
 
9.3%
e 28682
 
9.3%
r 23555
 
7.6%
g 15625
 
5.0%
u 15038
 
4.9%
l 10861
 
3.5%
o 10541
 
3.4%
10324
 
3.3%
Other values (25) 87649
28.3%

latitude
Real number (ℝ)

Distinct23668
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.602325
Minimum17.956533
Maximum67.1329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size422.5 KiB
2022-12-01T02:15:41.969259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum17.956533
5-th percentile28.84
Q132.170088
median34.40464
Q338.446068
95-th percentile46.512321
Maximum67.1329
Range49.176367
Interquartile range (IQR)6.2759802

Descriptive statistics

Standard deviation5.8066675
Coefficient of variation (CV)0.16309799
Kurtosis1.9616705
Mean35.602325
Median Absolute Deviation (MAD)2.7790671
Skewness0.4169243
Sum962758.07
Variance33.717387
MonotonicityNot monotonic
2022-12-01T02:15:42.383150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.993565 12
 
< 0.1%
47.8666 12
 
< 0.1%
47.8833 12
 
< 0.1%
33.3517 11
 
< 0.1%
33.3501 11
 
< 0.1%
35.2833 10
 
< 0.1%
35.3167 10
 
< 0.1%
18.403494 9
 
< 0.1%
35.0417 9
 
< 0.1%
33.3667 9
 
< 0.1%
Other values (23658) 26937
99.6%
ValueCountFrequency (%)
17.956533 3
< 0.1%
17.958364 6
< 0.1%
17.95838 2
 
< 0.1%
17.964052 1
 
< 0.1%
17.968477 1
 
< 0.1%
17.969381 4
< 0.1%
17.969784 1
 
< 0.1%
17.970539 7
< 0.1%
17.970869 7
< 0.1%
17.973274 1
 
< 0.1%
ValueCountFrequency (%)
67.1329 1
< 0.1%
67.0222 1
< 0.1%
66.9 1
< 0.1%
66.8333 1
< 0.1%
66.7303 1
< 0.1%
66.7215 1
< 0.1%
66.7069 1
< 0.1%
66.6938 1
< 0.1%
66.6163 1
< 0.1%
66.5663 1
< 0.1%

longitude
Real number (ℝ)

Distinct24825
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.935159
Minimum-165.1167
Maximum-65.417709
Zeros0
Zeros (%)0.0%
Negative27042
Negative (%)100.0%
Memory size422.5 KiB
2022-12-01T02:15:42.790062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-165.1167
5-th percentile-120.8112
Q1-97.771716
median-88.88195
Q3-81.934755
95-th percentile-74.137625
Maximum-65.417709
Range99.698991
Interquartile range (IQR)15.836961

Descriptive statistics

Standard deviation14.265275
Coefficient of variation (CV)-0.1551667
Kurtosis1.1811967
Mean-91.935159
Median Absolute Deviation (MAD)7.4290795
Skewness-1.0306004
Sum-2486110.6
Variance203.49807
MonotonicityNot monotonic
2022-12-01T02:15:43.184012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-66.386131 12
 
< 0.1%
-110.4573 12
 
< 0.1%
-67.081078 9
 
< 0.1%
-82.08 9
 
< 0.1%
-79.8 8
 
< 0.1%
-110.4507 8
 
< 0.1%
-110.434 8
 
< 0.1%
-67.23027 8
 
< 0.1%
-110.4673 8
 
< 0.1%
-79.5 7
 
< 0.1%
Other values (24815) 26953
99.7%
ValueCountFrequency (%)
-165.1167 1
< 0.1%
-162.5667 1
< 0.1%
-161.068 1
< 0.1%
-161.0167 1
< 0.1%
-160.12 1
< 0.1%
-159.8166 1
< 0.1%
-159.7614288 1
< 0.1%
-159.6652679 1
< 0.1%
-159.5889 1
< 0.1%
-159.5627441 1
< 0.1%
ValueCountFrequency (%)
-65.417709 3
< 0.1%
-65.43666667 1
 
< 0.1%
-65.44305556 1
 
< 0.1%
-65.472076 2
< 0.1%
-65.627907 1
 
< 0.1%
-65.64724 1
 
< 0.1%
-65.671127 1
 
< 0.1%
-65.685387 1
 
< 0.1%
-65.739037 1
 
< 0.1%
-65.790413 1
 
< 0.1%

Vegetation
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.362621
Minimum0
Maximum16
Zeros4156
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size422.5 KiB
2022-12-01T02:15:43.466258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median12
Q315
95-th percentile16
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.3573052
Coefficient of variation (CV)0.47148498
Kurtosis0.33775557
Mean11.362621
Median Absolute Deviation (MAD)3
Skewness-1.3019922
Sum307268
Variance28.700719
MonotonicityNot monotonic
2022-12-01T02:15:43.636827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 9107
33.7%
16 6116
22.6%
15 5326
19.7%
0 4156
15.4%
9 1656
 
6.1%
4 420
 
1.6%
14 261
 
1.0%
ValueCountFrequency (%)
0 4156
15.4%
4 420
 
1.6%
9 1656
 
6.1%
12 9107
33.7%
14 261
 
1.0%
15 5326
19.7%
16 6116
22.6%
ValueCountFrequency (%)
16 6116
22.6%
15 5326
19.7%
14 261
 
1.0%
12 9107
33.7%
9 1656
 
6.1%
4 420
 
1.6%
0 4156
15.4%

Temp_pre_30
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct18130
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.442991
Minimum-49.210526
Maximum46.6
Zeros1150
Zeros (%)4.3%
Negative7646
Negative (%)28.3%
Memory size422.5 KiB
2022-12-01T02:15:43.819346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-49.210526
5-th percentile-1
Q1-1
median9.4282054
Q319.697156
95-th percentile27.569292
Maximum46.6
Range95.810526
Interquartile range (IQR)20.697156

Descriptive statistics

Standard deviation10.427191
Coefficient of variation (CV)0.99848703
Kurtosis-1.1718252
Mean10.442991
Median Absolute Deviation (MAD)10.428205
Skewness0.30435404
Sum282399.36
Variance108.72631
MonotonicityNot monotonic
2022-12-01T02:15:44.005808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
 
26.1%
0 1150
 
4.3%
22 8
 
< 0.1%
1 7
 
< 0.1%
24.54446461 6
 
< 0.1%
13 6
 
< 0.1%
21 5
 
< 0.1%
8.85377193 5
 
< 0.1%
25 5
 
< 0.1%
-9.560578704 5
 
< 0.1%
Other values (18120) 18786
69.5%
ValueCountFrequency (%)
-49.21052632 2
< 0.1%
-18.8 1
< 0.1%
-14.6 1
< 0.1%
-14.16082317 1
< 0.1%
-13.73101412 1
< 0.1%
-13.67555273 1
< 0.1%
-13.54305395 1
< 0.1%
-13.44106275 1
< 0.1%
-13.37556713 1
< 0.1%
-13.3663348 1
< 0.1%
ValueCountFrequency (%)
46.6 1
< 0.1%
41.67781955 1
< 0.1%
40 1
< 0.1%
37.05099778 1
< 0.1%
37 1
< 0.1%
36.6957606 1
< 0.1%
36.53686007 1
< 0.1%
36 1
< 0.1%
35.62473348 1
< 0.1%
35.6 1
< 0.1%

Temp_pre_15
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct17380
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.515503
Minimum-37
Maximum51.567797
Zeros1881
Zeros (%)7.0%
Negative7421
Negative (%)27.4%
Memory size422.5 KiB
2022-12-01T02:15:44.405739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-37
5-th percentile-1
Q1-1
median9.5547819
Q319.833589
95-th percentile27.938002
Maximum51.567797
Range88.567797
Interquartile range (IQR)20.833589

Descriptive statistics

Standard deviation10.557321
Coefficient of variation (CV)1.0039768
Kurtosis-1.243337
Mean10.515503
Median Absolute Deviation (MAD)10.554782
Skewness0.33001338
Sum284360.24
Variance111.45703
MonotonicityNot monotonic
2022-12-01T02:15:44.575322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
 
26.1%
0 1881
 
7.0%
1 6
 
< 0.1%
25.16785714 6
 
< 0.1%
-7.751712963 5
 
< 0.1%
16.54180672 5
 
< 0.1%
11.35497186 5
 
< 0.1%
17 5
 
< 0.1%
13 5
 
< 0.1%
9 4
 
< 0.1%
Other values (17370) 18061
66.8%
ValueCountFrequency (%)
-37 1
< 0.1%
-18.8 1
< 0.1%
-17.36883538 1
< 0.1%
-16.93209076 1
< 0.1%
-13.85883306 2
< 0.1%
-13.78553241 1
< 0.1%
-13.27177177 1
< 0.1%
-13.04916667 1
< 0.1%
-12.47446168 1
< 0.1%
-12.3 1
< 0.1%
ValueCountFrequency (%)
51.56779661 1
< 0.1%
46 1
< 0.1%
40 1
< 0.1%
38.03043478 1
< 0.1%
37.85858586 1
< 0.1%
36.321875 1
< 0.1%
36.29787234 1
< 0.1%
36.20886076 1
< 0.1%
36.06473684 1
< 0.1%
36 1
< 0.1%

Temp_pre_7
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct16862
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.59418
Minimum-19.571131
Maximum52.014493
Zeros2209
Zeros (%)8.2%
Negative7380
Negative (%)27.3%
Memory size422.5 KiB
2022-12-01T02:15:44.754805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-19.571131
5-th percentile-1
Q1-1
median9.6180195
Q320.003542
95-th percentile28.252111
Maximum52.014493
Range71.585624
Interquartile range (IQR)21.003542

Descriptive statistics

Standard deviation10.69183
Coefficient of variation (CV)1.0092174
Kurtosis-1.2680299
Mean10.59418
Median Absolute Deviation (MAD)10.618019
Skewness0.33198911
Sum286487.81
Variance114.31524
MonotonicityNot monotonic
2022-12-01T02:15:44.987190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
 
26.1%
0 2209
 
8.2%
1 6
 
< 0.1%
25.203125 6
 
< 0.1%
24 5
 
< 0.1%
9 5
 
< 0.1%
26.5 5
 
< 0.1%
20 5
 
< 0.1%
-3.905704365 5
 
< 0.1%
18 5
 
< 0.1%
Other values (16852) 17732
65.6%
ValueCountFrequency (%)
-19.57113095 1
< 0.1%
-18.078 1
< 0.1%
-16.45995037 2
< 0.1%
-15.92083333 1
< 0.1%
-15.28992556 1
< 0.1%
-14.54761905 1
< 0.1%
-12.75374449 1
< 0.1%
-12.70327381 1
< 0.1%
-12.3 1
< 0.1%
-12.0901737 2
< 0.1%
ValueCountFrequency (%)
52.01449275 1
< 0.1%
50.5 1
< 0.1%
40 1
< 0.1%
38.52173913 1
< 0.1%
38.05882353 1
< 0.1%
37.63963964 1
< 0.1%
37.60824742 1
< 0.1%
36.08518519 1
< 0.1%
35.91538462 1
< 0.1%
35.69902913 1
< 0.1%

Temp_cont
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10452
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9947718
Minimum-14.017953
Maximum52
Zeros9186
Zeros (%)34.0%
Negative7145
Negative (%)26.4%
Memory size422.5 KiB
2022-12-01T02:15:45.229572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-14.017953
5-th percentile-1
Q1-1
median0
Q315.504126
95-th percentile26.811358
Maximum52
Range66.017953
Interquartile range (IQR)16.504126

Descriptive statistics

Standard deviation10.228598
Coefficient of variation (CV)1.4623205
Kurtosis-0.69824954
Mean6.9947718
Median Absolute Deviation (MAD)1
Skewness0.91881566
Sum189152.62
Variance104.62422
MonotonicityNot monotonic
2022-12-01T02:15:45.404927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9186
34.0%
-1 7059
26.1%
25.58614565 6
 
< 0.1%
22 6
 
< 0.1%
24 6
 
< 0.1%
30 5
 
< 0.1%
26 5
 
< 0.1%
29 4
 
< 0.1%
24.58767773 4
 
< 0.1%
9 4
 
< 0.1%
Other values (10442) 10757
39.8%
ValueCountFrequency (%)
-14.01795347 1
< 0.1%
-13.55364608 1
< 0.1%
-12.92462376 1
< 0.1%
-11.6681672 1
< 0.1%
-10.43845352 1
< 0.1%
-9.909219858 1
< 0.1%
-8.843117871 1
< 0.1%
-6.457162726 1
< 0.1%
-6.204580153 1
< 0.1%
-6.021276596 1
< 0.1%
ValueCountFrequency (%)
52 1
 
< 0.1%
39.4 1
 
< 0.1%
37.82857143 1
 
< 0.1%
37.2815534 1
 
< 0.1%
37.2 3
< 0.1%
36.99099099 1
 
< 0.1%
36.77 1
 
< 0.1%
36.36363636 1
 
< 0.1%
36 1
 
< 0.1%
35.92857143 1
 
< 0.1%

Wind_pre_30
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct17927
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8882152
Minimum-1
Maximum29.8
Zeros1318
Zeros (%)4.9%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:45.576777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2.3838492
Q33.4079893
95-th percentile4.8590279
Maximum29.8
Range30.8
Interquartile range (IQR)4.4079893

Descriptive statistics

Standard deviation2.0720062
Coefficient of variation (CV)1.0973359
Kurtosis0.16683279
Mean1.8882152
Median Absolute Deviation (MAD)1.3587456
Skewness-0.076964914
Sum51061.114
Variance4.2932099
MonotonicityNot monotonic
2022-12-01T02:15:45.752534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
 
26.1%
0 1318
 
4.9%
5.1 14
 
0.1%
3.1 13
 
< 0.1%
2.6 10
 
< 0.1%
3.6 9
 
< 0.1%
2.1 6
 
< 0.1%
3.862659381 6
 
< 0.1%
4.0025 5
 
< 0.1%
2.015899582 5
 
< 0.1%
Other values (17917) 18597
68.8%
ValueCountFrequency (%)
-1 7059
26.1%
0 1318
 
4.9%
0.025694444 1
 
< 0.1%
0.23 1
 
< 0.1%
0.259701493 1
 
< 0.1%
0.273333333 1
 
< 0.1%
0.27826087 1
 
< 0.1%
0.281058496 1
 
< 0.1%
0.319148936 1
 
< 0.1%
0.349442379 1
 
< 0.1%
ValueCountFrequency (%)
29.8 1
< 0.1%
12.79207921 1
< 0.1%
10.78500311 1
< 0.1%
10.37078652 1
< 0.1%
9.131582569 1
< 0.1%
8.760196575 1
< 0.1%
8.701117318 1
< 0.1%
8.7 1
< 0.1%
8.521369249 1
< 0.1%
8.519079592 1
< 0.1%

Wind_pre_15
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct17108
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8006023
Minimum-1
Maximum29.8
Zeros2056
Zeros (%)7.6%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:46.023995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2.2500933
Q33.3789762
95-th percentile4.8659127
Maximum29.8
Range30.8
Interquartile range (IQR)4.3789762

Descriptive statistics

Standard deviation2.0889627
Coefficient of variation (CV)1.1601467
Kurtosis0.13221591
Mean1.8006023
Median Absolute Deviation (MAD)1.5905002
Skewness0.041896936
Sum48691.886
Variance4.3637653
MonotonicityNot monotonic
2022-12-01T02:15:46.287064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
 
26.1%
0 2056
 
7.6%
3.1 13
 
< 0.1%
3.6 9
 
< 0.1%
5.1 8
 
< 0.1%
3.547857143 6
 
< 0.1%
2.6 6
 
< 0.1%
3.35 5
 
< 0.1%
2.029131653 5
 
< 0.1%
3.532768362 5
 
< 0.1%
Other values (17098) 17870
66.1%
ValueCountFrequency (%)
-1 7059
26.1%
0 2056
 
7.6%
0.017250324 1
 
< 0.1%
0.017334778 1
 
< 0.1%
0.031388889 1
 
< 0.1%
0.078947368 1
 
< 0.1%
0.204559271 1
 
< 0.1%
0.236142132 1
 
< 0.1%
0.298885794 1
 
< 0.1%
0.317316017 1
 
< 0.1%
ValueCountFrequency (%)
29.8 1
< 0.1%
14.07932011 1
< 0.1%
10.59253499 1
< 0.1%
9.929775281 1
< 0.1%
9.23631516 1
< 0.1%
8.755936675 1
< 0.1%
8.616784824 1
< 0.1%
8.595044423 1
< 0.1%
8.589339784 1
< 0.1%
8.528720856 1
< 0.1%

Wind_pre_7
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct16376
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7516269
Minimum-1
Maximum15.706587
Zeros2383
Zeros (%)8.8%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:47.366438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2.1337285
Q33.3358333
95-th percentile4.9698913
Maximum15.706587
Range16.706587
Interquartile range (IQR)4.3358333

Descriptive statistics

Standard deviation2.11083
Coefficient of variation (CV)1.2050682
Kurtosis-0.86257891
Mean1.7516269
Median Absolute Deviation (MAD)1.7766436
Skewness0.091703188
Sum47367.495
Variance4.4556031
MonotonicityNot monotonic
2022-12-01T02:15:47.670622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
26.1%
0 2383
 
8.8%
3.1 14
 
0.1%
3.6 12
 
< 0.1%
2.5 8
 
< 0.1%
1.55 6
 
< 0.1%
4.025 6
 
< 0.1%
2 5
 
< 0.1%
1.78969697 5
 
< 0.1%
4.553571429 5
 
< 0.1%
Other values (16366) 17539
64.9%
ValueCountFrequency (%)
-1 7059
26.1%
0 2383
 
8.8%
0.012560386 1
 
< 0.1%
0.018452381 1
 
< 0.1%
0.033962264 1
 
< 0.1%
0.045070423 1
 
< 0.1%
0.159589041 1
 
< 0.1%
0.256547619 1
 
< 0.1%
0.275 1
 
< 0.1%
0.298901099 1
 
< 0.1%
ValueCountFrequency (%)
15.70658683 1
< 0.1%
12.68167331 1
< 0.1%
11.62857143 1
< 0.1%
10.75 1
< 0.1%
10.24896104 1
< 0.1%
9.927710843 1
< 0.1%
9.547619048 1
< 0.1%
9.32 2
< 0.1%
9.08 1
< 0.1%
9.073546645 1
< 0.1%

Wind_cont
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10318
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96588479
Minimum-1
Maximum9.8280702
Zeros9255
Zeros (%)34.2%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:47.932921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q32.6266176
95-th percentile4.3910676
Maximum9.8280702
Range10.82807
Interquartile range (IQR)3.6266176

Descriptive statistics

Standard deviation1.9093055
Coefficient of variation (CV)1.9767424
Kurtosis-0.52592732
Mean0.96588479
Median Absolute Deviation (MAD)1
Skewness0.75849368
Sum26119.457
Variance3.6454474
MonotonicityNot monotonic
2022-12-01T02:15:48.327868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9255
34.2%
-1 7059
26.1%
5.1 10
 
< 0.1%
3.6 8
 
< 0.1%
2.6 8
 
< 0.1%
7.7 7
 
< 0.1%
3.9 6
 
< 0.1%
3.434458259 6
 
< 0.1%
3.1 5
 
< 0.1%
3.15 5
 
< 0.1%
Other values (10308) 10673
39.5%
ValueCountFrequency (%)
-1 7059
26.1%
0 9255
34.2%
0.155963303 1
 
< 0.1%
0.229464286 1
 
< 0.1%
0.260377358 1
 
< 0.1%
0.262121212 1
 
< 0.1%
0.284 1
 
< 0.1%
0.305555556 1
 
< 0.1%
0.308148148 1
 
< 0.1%
0.320059142 1
 
< 0.1%
ValueCountFrequency (%)
9.828070175 1
< 0.1%
9.625 1
< 0.1%
9.065248227 1
< 0.1%
8.925 1
< 0.1%
8.832672779 1
< 0.1%
8.72413218 1
< 0.1%
8.654166667 1
< 0.1%
8.579407855 1
< 0.1%
8.477721717 1
< 0.1%
8.45 1
< 0.1%

Hum_pre_30
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct17576
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.268777
Minimum-1
Maximum96
Zeros1721
Zeros (%)6.4%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:48.646052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median58.704951
Q368.187365
95-th percentile76.331822
Maximum96
Range97
Interquartile range (IQR)69.187365

Descriptive statistics

Standard deviation31.485219
Coefficient of variation (CV)0.74488122
Kurtosis-1.5290542
Mean42.268777
Median Absolute Deviation (MAD)14.239326
Skewness-0.4902609
Sum1143032.3
Variance991.31899
MonotonicityNot monotonic
2022-12-01T02:15:49.256381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
 
26.1%
0 1721
 
6.4%
52 7
 
< 0.1%
74.61313869 6
 
< 0.1%
67 6
 
< 0.1%
60.77708703 5
 
< 0.1%
66 5
 
< 0.1%
61 5
 
< 0.1%
88 5
 
< 0.1%
64.21649485 4
 
< 0.1%
Other values (17566) 18219
67.4%
ValueCountFrequency (%)
-1 7059
26.1%
0 1721
 
6.4%
6 1
 
< 0.1%
9.351165981 1
 
< 0.1%
11 3
 
< 0.1%
11.93931034 1
 
< 0.1%
12 3
 
< 0.1%
12.04931507 1
 
< 0.1%
12.53714286 1
 
< 0.1%
12.90135135 1
 
< 0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
94 4
< 0.1%
93.91666667 1
 
< 0.1%
92.14285714 1
 
< 0.1%
92.08163265 1
 
< 0.1%
91.46666667 1
 
< 0.1%
91.27083333 1
 
< 0.1%
90.5 1
 
< 0.1%
90.38095238 1
 
< 0.1%
90 1
 
< 0.1%

Hum_pre_15
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct16814
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.837107
Minimum-1
Maximum94
Zeros2448
Zeros (%)9.1%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:49.679251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median55.421184
Q366.869037
95-th percentile76.124824
Maximum94
Range95
Interquartile range (IQR)67.869037

Descriptive statistics

Standard deviation31.544955
Coefficient of variation (CV)0.79184856
Kurtosis-1.6360649
Mean39.837107
Median Absolute Deviation (MAD)17.166029
Skewness-0.35939842
Sum1077275
Variance995.08421
MonotonicityNot monotonic
2022-12-01T02:15:50.045307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
26.1%
0 2448
 
9.1%
75.2437276 6
 
< 0.1%
68 6
 
< 0.1%
61 5
 
< 0.1%
58 5
 
< 0.1%
51.9752381 5
 
< 0.1%
64 4
 
< 0.1%
55 4
 
< 0.1%
60 4
 
< 0.1%
Other values (16804) 17496
64.7%
ValueCountFrequency (%)
-1 7059
26.1%
0 2448
 
9.1%
6 1
 
< 0.1%
6.4 1
 
< 0.1%
7.547945205 1
 
< 0.1%
8.452777778 1
 
< 0.1%
10.99152542 1
 
< 0.1%
11 3
 
< 0.1%
11.96542894 1
 
< 0.1%
12 3
 
< 0.1%
ValueCountFrequency (%)
94 3
< 0.1%
93.75 1
 
< 0.1%
93.45454545 1
 
< 0.1%
93.14285714 1
 
< 0.1%
93 1
 
< 0.1%
91.63562753 1
 
< 0.1%
91.47321429 1
 
< 0.1%
91.44444444 1
 
< 0.1%
91.14857143 1
 
< 0.1%
90.8422619 1
 
< 0.1%

Hum_pre_7
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct16188
Distinct (%)59.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.398726
Minimum-1
Maximum95.24
Zeros2764
Zeros (%)10.2%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:52.091796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median51.980045
Q365.825821
95-th percentile76.613724
Maximum95.24
Range96.24
Interquartile range (IQR)66.825821

Descriptive statistics

Standard deviation31.390813
Coefficient of variation (CV)0.81749621
Kurtosis-1.657285
Mean38.398726
Median Absolute Deviation (MAD)20.462559
Skewness-0.27454689
Sum1038378.3
Variance985.38314
MonotonicityNot monotonic
2022-12-01T02:15:52.994383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7059
26.1%
0 2764
 
10.2%
70 12
 
< 0.1%
48 6
 
< 0.1%
69 6
 
< 0.1%
74.9921875 6
 
< 0.1%
49 6
 
< 0.1%
41 6
 
< 0.1%
78 5
 
< 0.1%
66 5
 
< 0.1%
Other values (16178) 17167
63.5%
ValueCountFrequency (%)
-1 7059
26.1%
0 2764
 
10.2%
5.976608187 1
 
< 0.1%
6 1
 
< 0.1%
6.4 1
 
< 0.1%
8.146814404 1
 
< 0.1%
8.883333333 1
 
< 0.1%
9.994047619 1
 
< 0.1%
10.32738095 1
 
< 0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
95.24 1
 
< 0.1%
94 5
< 0.1%
93.51351351 1
 
< 0.1%
92.51376147 1
 
< 0.1%
91.52727273 1
 
< 0.1%
91.17647059 1
 
< 0.1%
91 1
 
< 0.1%
90.87272727 1
 
< 0.1%
90.73049645 1
 
< 0.1%
90.70697674 1
 
< 0.1%

Hum_cont
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct10171
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.250195
Minimum-1
Maximum94
Zeros9435
Zeros (%)34.9%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:53.937858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q361.354874
95-th percentile74.727785
Maximum94
Range95
Interquartile range (IQR)62.354874

Descriptive statistics

Standard deviation31.783454
Coefficient of variation (CV)1.3106473
Kurtosis-1.5042622
Mean24.250195
Median Absolute Deviation (MAD)1
Skewness0.59596593
Sum655773.78
Variance1010.1879
MonotonicityNot monotonic
2022-12-01T02:15:54.495380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9435
34.9%
-1 7059
26.1%
75.52669039 6
 
< 0.1%
64 5
 
< 0.1%
44 5
 
< 0.1%
65 5
 
< 0.1%
57 4
 
< 0.1%
88 4
 
< 0.1%
69.73205742 4
 
< 0.1%
67.48527132 4
 
< 0.1%
Other values (10161) 10511
38.9%
ValueCountFrequency (%)
-1 7059
26.1%
0 9435
34.9%
7.458333333 1
 
< 0.1%
8.576923077 1
 
< 0.1%
9.166666667 1
 
< 0.1%
9.6 1
 
< 0.1%
10.42857143 1
 
< 0.1%
10.47368421 1
 
< 0.1%
10.70588235 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
94 2
< 0.1%
93 1
< 0.1%
91.29859719 1
< 0.1%
91.27777778 1
< 0.1%
91.13333333 1
< 0.1%
90.63978495 1
< 0.1%
90.53571429 1
< 0.1%
90.2962963 1
< 0.1%
90 2
< 0.1%
89.96 1
< 0.1%

Prec_pre_30
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2142
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.342375
Minimum-1
Maximum13560.8
Zeros9628
Zeros (%)35.6%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:54.859393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q324.4
95-th percentile128.095
Maximum13560.8
Range13561.8
Interquartile range (IQR)25.4

Descriptive statistics

Standard deviation126.19056
Coefficient of variation (CV)4.4523635
Kurtosis5038.7801
Mean28.342375
Median Absolute Deviation (MAD)1
Skewness52.349206
Sum766434.5
Variance15924.056
MonotonicityNot monotonic
2022-12-01T02:15:55.039250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9628
35.6%
-1 7059
26.1%
0.3 128
 
0.5%
0.8 88
 
0.3%
0.5 83
 
0.3%
1 60
 
0.2%
1.8 57
 
0.2%
3.3 43
 
0.2%
1.3 43
 
0.2%
4.1 43
 
0.2%
Other values (2132) 9810
36.3%
ValueCountFrequency (%)
-1 7059
26.1%
0 9628
35.6%
0.3 128
 
0.5%
0.5 83
 
0.3%
0.6 29
 
0.1%
0.8 88
 
0.3%
0.9 8
 
< 0.1%
1 60
 
0.2%
1.1 22
 
0.1%
1.2 7
 
< 0.1%
ValueCountFrequency (%)
13560.8 1
< 0.1%
4936.7 1
< 0.1%
2839 1
< 0.1%
2797 1
< 0.1%
2527 2
< 0.1%
2214 1
< 0.1%
2179 1
< 0.1%
2177 1
< 0.1%
2100 1
< 0.1%
1898 1
< 0.1%

Prec_pre_15
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1347
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.407063
Minimum-1
Maximum2527
Zeros11040
Zeros (%)40.8%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:55.288989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q35.3
95-th percentile61.5
Maximum2527
Range2528
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation51.697709
Coefficient of variation (CV)4.1667966
Kurtosis455.39946
Mean12.407063
Median Absolute Deviation (MAD)1
Skewness16.480217
Sum335511.8
Variance2672.6531
MonotonicityNot monotonic
2022-12-01T02:15:55.476477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11040
40.8%
-1 7059
26.1%
0.3 287
 
1.1%
0.5 138
 
0.5%
0.8 130
 
0.5%
1.3 105
 
0.4%
1 105
 
0.4%
0.6 79
 
0.3%
1.5 79
 
0.3%
1.8 74
 
0.3%
Other values (1337) 7946
29.4%
ValueCountFrequency (%)
-1 7059
26.1%
0 11040
40.8%
0.3 287
 
1.1%
0.5 138
 
0.5%
0.6 79
 
0.3%
0.8 130
 
0.5%
0.9 18
 
0.1%
1 105
 
0.4%
1.1 40
 
0.1%
1.2 11
 
< 0.1%
ValueCountFrequency (%)
2527 1
< 0.1%
1638 2
< 0.1%
1400 1
< 0.1%
1258 1
< 0.1%
1250 1
< 0.1%
1248 1
< 0.1%
1210 1
< 0.1%
1202.6 1
< 0.1%
1187 1
< 0.1%
1166 1
< 0.1%

Prec_pre_7
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct850
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0177428
Minimum-1
Maximum1250
Zeros13242
Zeros (%)49.0%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:55.632656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q30
95-th percentile27.695
Maximum1250
Range1251
Interquartile range (IQR)1

Descriptive statistics

Standard deviation28.550466
Coefficient of variation (CV)5.6899023
Kurtosis608.10318
Mean5.0177428
Median Absolute Deviation (MAD)0.3
Skewness19.992356
Sum135689.8
Variance815.12913
MonotonicityNot monotonic
2022-12-01T02:15:55.827485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13242
49.0%
-1 7059
26.1%
0.3 478
 
1.8%
0.5 201
 
0.7%
0.8 177
 
0.7%
1 162
 
0.6%
0.6 142
 
0.5%
1.3 141
 
0.5%
1.8 109
 
0.4%
2 102
 
0.4%
Other values (840) 5229
 
19.3%
ValueCountFrequency (%)
-1 7059
26.1%
0 13242
49.0%
0.3 478
 
1.8%
0.5 201
 
0.7%
0.6 142
 
0.5%
0.8 177
 
0.7%
0.9 21
 
0.1%
1 162
 
0.6%
1.1 37
 
0.1%
1.2 21
 
0.1%
ValueCountFrequency (%)
1250 1
 
< 0.1%
1145 1
 
< 0.1%
1136 1
 
< 0.1%
1088 1
 
< 0.1%
1068 1
 
< 0.1%
703.7 5
< 0.1%
605 1
 
< 0.1%
585.2 1
 
< 0.1%
533.4 1
 
< 0.1%
526 1
 
< 0.1%

Prec_cont
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1868
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.144449
Minimum-1
Maximum2126
Zeros14564
Zeros (%)53.9%
Negative7059
Negative (%)26.1%
Memory size422.5 KiB
2022-12-01T02:15:56.059901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q30
95-th percentile105.79
Maximum2126
Range2127
Interquartile range (IQR)1

Descriptive statistics

Standard deviation61.44719
Coefficient of variation (CV)3.8060877
Kurtosis270.71457
Mean16.144449
Median Absolute Deviation (MAD)0
Skewness12.169052
Sum436578.2
Variance3775.7572
MonotonicityNot monotonic
2022-12-01T02:15:56.404830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14564
53.9%
-1 7059
26.1%
0.3 86
 
0.3%
0.5 42
 
0.2%
0.8 35
 
0.1%
1 32
 
0.1%
1.3 24
 
0.1%
1.8 22
 
0.1%
6.9 17
 
0.1%
0.6 17
 
0.1%
Other values (1858) 5144
 
19.0%
ValueCountFrequency (%)
-1 7059
26.1%
0 14564
53.9%
0.3 86
 
0.3%
0.5 42
 
0.2%
0.6 17
 
0.1%
0.8 35
 
0.1%
0.9 5
 
< 0.1%
1 32
 
0.1%
1.1 6
 
< 0.1%
1.2 9
 
< 0.1%
ValueCountFrequency (%)
2126 1
< 0.1%
2021 1
< 0.1%
1906 1
< 0.1%
1743 1
< 0.1%
1650.6 1
< 0.1%
1565 1
< 0.1%
1547.5 1
< 0.1%
1483.1 1
< 0.1%
1468.3 1
< 0.1%
1298 1
< 0.1%

remoteness
Real number (ℝ)

Distinct26510
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22777694
Minimum0.00039029609
Maximum0.98889249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size422.5 KiB
2022-12-01T02:15:56.759701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.00039029609
5-th percentile0.061702045
Q10.13559236
median0.19662318
Q30.28049181
95-th percentile0.50349374
Maximum0.98889249
Range0.98850219
Interquartile range (IQR)0.14489945

Descriptive statistics

Standard deviation0.13465917
Coefficient of variation (CV)0.59118876
Kurtosis1.9873484
Mean0.22777694
Median Absolute Deviation (MAD)0.066789234
Skewness1.2197751
Sum6159.5439
Variance0.018133091
MonotonicityNot monotonic
2022-12-01T02:15:57.015707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01499337293 12
 
< 0.1%
0.0203076585 9
 
< 0.1%
0.02167560651 8
 
< 0.1%
0.01732926128 7
 
< 0.1%
0.02086862437 7
 
< 0.1%
0.01551030113 7
 
< 0.1%
0.02029442702 7
 
< 0.1%
0.01385533322 7
 
< 0.1%
0.0138406465 7
 
< 0.1%
0.01487451004 7
 
< 0.1%
Other values (26500) 26964
99.7%
ValueCountFrequency (%)
0.000390296094 1
< 0.1%
0.0005486117097 1
< 0.1%
0.00115808082 1
< 0.1%
0.001432680685 1
< 0.1%
0.001603632794 1
< 0.1%
0.001914237784 1
< 0.1%
0.001919898212 1
< 0.1%
0.00221340794 1
< 0.1%
0.003069259474 1
< 0.1%
0.003139979534 1
< 0.1%
ValueCountFrequency (%)
0.98889249 1
< 0.1%
0.9613480023 1
< 0.1%
0.9433846104 1
< 0.1%
0.9432211902 1
< 0.1%
0.9417922698 1
< 0.1%
0.9408166977 1
< 0.1%
0.9396593746 1
< 0.1%
0.9383646617 1
< 0.1%
0.9383610175 1
< 0.1%
0.9382027438 1
< 0.1%

Interactions

2022-12-01T02:15:35.180750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:41.603457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:45.207667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:51.198225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:55.541709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:59.446241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:03.459836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:08.449041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:12.550234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:20.757952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:27.799115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:32.169480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:38.100616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:44.114564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:49.591878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:54.664311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:59.985076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:04.931845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:13.265553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:17.697387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:26.232680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:35.365253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:41.790076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:45.410125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:51.679487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:55.714435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:59.605803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:03.620040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:08.617690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:12.712297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:21.233678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:27.998584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:32.375927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:38.950343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:44.318021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:49.784398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:54.994427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:00.296245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:05.149264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:13.524858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:17.886729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:27.360662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:35.540782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:41.945470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:45.578461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:51.954946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:55.865996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:59.790346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:03.782521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:08.773649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:12.868505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:21.623637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:28.201043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:32.584426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:39.326336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:44.501530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:49.968870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:55.335518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:00.812861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:05.367678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:13.693409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:18.263993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:28.522553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:35.809067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:42.102617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:45.829813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:52.229942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:56.040476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:59.957513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:03.931586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:08.933596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:13.009097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:22.011620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:28.394526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:32.828717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:39.673409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:44.691983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:50.351881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:55.557918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:01.074172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:05.805518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:13.861995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:18.578156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:29.267560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:36.079339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:42.292164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:46.110100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:52.441454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:56.232690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:00.142442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:04.268564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:09.107512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:13.261841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:22.415521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:28.603020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:33.242612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:40.027460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:44.969251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:50.662052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:55.743459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:01.363394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:06.087753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:14.102314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:18.964130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:30.466354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:36.271823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:42.451525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:46.297332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:52.579376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:15:03.541568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:10.941768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:16.261260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:22.515622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:33.577032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:38.356045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:44.119640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:49.179701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:54.398149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:58.339325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:02.244033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:07.138616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:11.084661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:18.592742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:14:42.974578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:47.978196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:15:33.750570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:38.510670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:44.290853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:13:54.545431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:58.495374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:02.401680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:07.397973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:11.293761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:18.801185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:26.698061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:31.031561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:36.206681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:43.153099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:48.438961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:53.356841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:58.310565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:03.952463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:13:54.709588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:14:07.603973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:14:19.197127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:26.912488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:31.238968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:36.592650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:43.381527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:48.802988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:53.580209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:58.500093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:04.154924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:11.958049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:16.812008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:23.585758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:34.166455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:38.930509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:44.634007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:50.361861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:54.862499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:58.896417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:02.833636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:07.763837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:11.761036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:19.507298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:27.111954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:31.430460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:36.943710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:43.561009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:48.996507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:53.826550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:58.666642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:04.351397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:12.287170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:17.100312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:23.932831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:34.426760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:39.113057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:44.794497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:50.660914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:55.030783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:59.097295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:03.138782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:08.099168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:11.981297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:19.848386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:27.413148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:31.717696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:37.258866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:43.739532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:49.207915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:54.140709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:58.982758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:04.547871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:12.575398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:17.360987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:24.389609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:34.612302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:39.302549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:44.984986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:50.922016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:55.382381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:13:59.268342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:14:08.295002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-01T02:14:27.605635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:31.907183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:37.526151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:43.921046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:49.387461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:54.401051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:14:59.401637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:04.735380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:12.925462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:17.526617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:25.565463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-01T02:15:34.897501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-01T02:15:57.361476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-01T02:15:58.700639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-01T02:15:59.614153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-01T02:16:00.712598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-01T02:16:01.595803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-01T02:15:39.591738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-01T02:15:40.278902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fire_sizestat_cause_descrlatitudelongitudeVegetationTemp_pre_30Temp_pre_15Temp_pre_7Temp_contWind_pre_30Wind_pre_15Wind_pre_7Wind_contHum_pre_30Hum_pre_15Hum_pre_7Hum_contPrec_pre_30Prec_pre_15Prec_pre_7Prec_contremoteness
13.0Arson35.03833-87.610000157.5534337.0100000.34352910.4482982.7097642.8817071.9764712.12232070.84000065.85891155.50588281.68267859.88.40.086.80.184355
31.0Debris Burning39.64140-119.308300016.27596718.99618118.1425640.0000004.0549823.3983293.6712820.00000044.77842937.14081135.3538460.00000010.47.20.00.00.487447
42.0Miscellaneous30.70060-90.59140012-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0-1.0-1.0-1.00.214633
51.0Debris Burning32.06390-82.41780012-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0-1.0-1.0-1.00.139643
71.0Campfire30.90472-93.5575001216.85193916.99778320.43478311.9855601.3312571.4729491.4247832.14885772.89947875.06138177.92462370.73291128.427.51.255.40.241894
81.0Arson35.90031-92.0611801526.65524127.26487028.96806428.6826881.7680741.7052971.8279442.10309068.31902267.57541965.07784460.1968586.63.30.046.40.224629
91.0Miscellaneous48.83940-99.718500154.6009506.8618786.0533330.0000006.3807606.3342546.6453330.00000064.60650955.94303854.3378380.00000012.31.80.00.00.291683
121.0Debris Burning33.85574-85.311075160.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.00.00.00.164315
131.0Fireworks31.76738-93.1468701226.38449326.06816726.51904826.9273031.7716391.6297432.0190481.38218279.94001279.94212267.63095276.70679262.84.30.056.20.237431
143.0Missing/Undefined32.77500-80.65833312-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0-1.0-1.0-1.00.123518
fire_sizestat_cause_descrlatitudelongitudeVegetationTemp_pre_30Temp_pre_15Temp_pre_7Temp_contWind_pre_30Wind_pre_15Wind_pre_7Wind_contHum_pre_30Hum_pre_15Hum_pre_7Hum_contPrec_pre_30Prec_pre_15Prec_pre_7Prec_contremoteness
499831.30Arson33.379270-79.91393016-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0-1.0-1.0-1.00.116525
499851.00Miscellaneous31.833413-99.0445621227.67896028.67666427.09919028.5573933.8189604.1246492.7352233.42281550.84871642.87535138.21862355.6013610.00.00.00.00.292726
499862.00Miscellaneous37.582200-82.7617000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0-1.0-1.0-1.00.139232
499881.10Debris Burning33.717731-83.228058168.1698648.0449449.9423460.0000001.5220871.6523411.6739560.00000073.00335468.94666774.3685340.0000000.00.00.00.00.145718
499901.00Smoking40.993889-121.6400001521.46225720.24167519.71237317.2189891.4403231.5280951.4294121.59943338.16176536.44666732.01622743.8587620.00.00.00.00.510716
499911.00Debris Burning32.801996-96.1228431212.69068513.0164127.60671115.6260004.7546044.7889313.9298664.71635266.81828174.84435886.34375065.37087694.094.094.077.20.264463
499941.00Miscellaneous37.976100-90.75900009.16211211.54031512.9502980.0000004.2413624.2541245.1630220.00000061.65778463.73339658.1763530.00000091.669.416.00.00.211468
499951.00Children47.270800-95.62140015-9.592386-0.4042994.7230770.0000003.5371253.9189554.3700440.00000074.90150378.07112576.3892620.0000000.00.00.00.00.253276
499971.16Debris Burning31.035600-83.17750012-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0-1.0-1.0-1.00.147185
499991.50Children29.316670-82.133330129.5191490.0000000.0000000.0000001.3282980.0000000.0000000.00000069.0250570.0000000.0000000.00000044.20.00.00.00.139237

Duplicate rows

Most frequently occurring

fire_sizestat_cause_descrlatitudelongitudeVegetationTemp_pre_30Temp_pre_15Temp_pre_7Temp_contWind_pre_30Wind_pre_15Wind_pre_7Wind_contHum_pre_30Hum_pre_15Hum_pre_7Hum_contPrec_pre_30Prec_pre_15Prec_pre_7Prec_contremoteness# duplicates
81.0Missing/Undefined17.970539-66.24641412-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0138555
111.0Missing/Undefined17.993565-66.38613112-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0149935
161.0Missing/Undefined18.016670-66.45140812-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0155105
91.0Missing/Undefined17.970869-66.36858412-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0148754
392.0Missing/Undefined17.993565-66.38613112-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0149934
71.0Missing/Undefined17.958364-66.17285212-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0132563
101.0Missing/Undefined17.981108-65.93803412-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0112713
171.0Missing/Undefined18.025436-66.59626812-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0167103
181.0Missing/Undefined18.036682-66.25429512-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0138413
271.0Missing/Undefined18.456905-66.47052012-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.0-1.00.0151383